Online Transient Stability Fault Screening Based on Support Vector Machine

被引：0
|
作者：
Bao Y. [1 ]
Feng C. [2 ]
Ren X. [1 ]
Zhang J. [1 ]
Ma C. [2 ]
Shao W. [1 ]
机构：
[1] NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing
[2] National Electric Power Dispatching and Control Center, State Grid Corporation of China, Beijing
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 22期
关键词：
Fault screening; Online assessment; Quantitative assessment; Support vector machine (SVM); Transient stability;
D O I：
10.7500/AEPS20190507005
中图分类号：
学科分类号：
摘要：
To meet the timeliness requirements of online transient stability assessment, a fault screening method based on support vector machine (SVM) and historical big data is proposed. Combined with the transient stability quantitative evaluation method of extended equal-area criterion (EEAC) and based on the system power angle stability mode and the generator participation factor, the feature variables are identified and the historical data are clustered. The instable samples are predicted by classification and regression. The predicted stability margin, classification stability prediction results and reliability are obtained by the applicability discrimination and model matching. Interactive parallel computing is used to screen online transient stability faults, which could avoid the inherent misjudgment of transient stability assessment by using SVM method. The effectiveness of the method is verified by an actual case. © 2019 Automation of Electric Power Systems Press.
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页码：52 / 58
页数：6
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